{smcl}
{txt}{sf}{ul off}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/Users/carlosxabel/Dropbox/HigherEd/Learning to love JOP data/Output/4Additionalempirical/4Log.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}27 Feb 2024, 16:27:33
{txt}
{com}. 
. 
.  krls forspending20051992 l_sh_routine33a_logtotalrev90imp  
{res}{txt}Iteration =  1, Looloss: 4.837128  
{txt}Iteration =  2, Looloss: 4.822193  
{txt}Iteration =  3, Looloss: 4.797427  
{txt}Iteration =  4, Looloss: 4.761725  
{txt}Iteration =  5, Looloss: 4.718404  
{txt}Iteration =  6, Looloss: 4.674628  
{txt}Iteration =  7, Looloss: 4.637417  
{txt}Iteration =  8, Looloss: 4.609484  
{txt}Iteration =  9, Looloss: 4.588935  
{txt}Iteration = 10, Looloss: 4.572409  
{txt}Iteration = 11, Looloss: 4.557968  
{txt}Iteration = 12, Looloss: 4.545612  
{txt}Iteration = 13, Looloss: 4.536074  
{res}
{txt}Pointwise Derivatives{txt}{space 55} Number of obs = {res}      63 
{txt}{space 76} Lambda {space 6} = {res}  .07269 
{txt}{space 76} Tolerance {space 3} = {res}    .063 
{txt}{space 76} Sigma {space 6}  = {res}       1 
{txt}{space 76} Eff. df {space 4}  = {res}   3.517 
{txt}{space 76} R2 {space 11}= {res}   .1484 
{txt}{space 76} Looloss {space 6}= {res}   4.526

{txt}{space 12}forspending20051992 {c |}      Avg.{space 7}SE{space 8}t{space 4}P>|t|{space 8}P25{space 7}P50{space 7}P75{space 7}
{hline 32}{c +}{hline 68}
{space  1}{txt}l_sh_routine33a_logtotalrev90i {c |} {res} .100921 {space 1}{res} .047898{res}    2.107{res}    0.039 {space 1} {res} .037724 {space 1}{res} .037724 {space 1}{res} .088925 {space 1}
{txt}{hline 32}{c +}{hline 68}


{com}.   predict Forspending_KRLS
{res}{txt}
{com}.   
.   twoway (scatter forspending20051992 l_sh_routine33a_logtotalrev90imp, sort) ///
>     (line Forspending_KRLS l_sh_routine33a_logtotalrev90imp, sort), graphregion(color(white)) xtitle(Routine Share X HE investment) leg(off)
{res}{txt}
{com}.   
.   
. graph export "ColoradoSupport.png", replace
{txt}(file /Users/carlosxabel/Dropbox/HigherEd/Learning to love JOP data/Output/4Additionalempirical/ColoradoSupport.png written in PNG format)

{com}. 
. 
. drop Forspending_KRLS
{txt}
{com}.         
.         
.         ********Wisconsin
.         
. cd ../..
{res}/Users/carlosxabel/Dropbox/HigherEd/Learning to love JOP data
{txt}
{com}.         
. use "Data/WisconsinBadgerpoll.dta", clear
{txt}
{com}. 
. cd "Output/4Additionalempirical"
{res}/Users/carlosxabel/Dropbox/HigherEd/Learning to love JOP data/Output/4Additionalempirical
{txt}
{com}.         
.         
.         global controls nonwhite conservative moderate collegegrad
{txt}
{com}.   
.   
.    logit higherhepriority  c.l_sh_routine33a##c.logtotalrev90imp  l_shind_manuf_cbp l_sh_popedu_c  l_sh_popfborn l_sh_empl_f    popdensity $controls,r

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-318.66946}  
Iteration 1:{space 3}log pseudolikelihood = {res:-301.04354}  
Iteration 2:{space 3}log pseudolikelihood = {res:-300.97376}  
Iteration 3:{space 3}log pseudolikelihood = {res:-300.97375}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       462
{txt}{col 49}Wald chi2({res}12{txt}){col 67}= {res}     31.71
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0015
{txt}Log pseudolikelihood = {res}-300.97375{txt}{col 49}Pseudo R2{col 67}= {res}    0.0555

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}    Robust
{col 1}                    higherhepriority{col 38}{c |}      Coef.{col 50}   Std. Err.{col 62}      z{col 70}   P>|z|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}l_sh_routine33a {c |}{col 38}{res}{space 2}-31.49897{col 50}{space 2} 18.52483{col 61}{space 1}   -1.70{col 70}{space 3}0.089{col 78}{space 4}-67.80697{col 91}{space 3}  4.80904
{txt}{space 20}logtotalrev90imp {c |}{col 38}{res}{space 2}-.9404929{col 50}{space 2} .9190429{col 61}{space 1}   -1.02{col 70}{space 3}0.306{col 78}{space 4}-2.741784{col 91}{space 3} .8607981
{txt}{space 36} {c |}
c.l_sh_routine33a#c.logtotalrev90imp {c |}{col 38}{res}{space 2} 2.931345{col 50}{space 2} 2.919295{col 61}{space 1}    1.00{col 70}{space 3}0.315{col 78}{space 4}-2.790368{col 91}{space 3} 8.653058
{txt}{space 36} {c |}
{space 19}l_shind_manuf_cbp {c |}{col 38}{res}{space 2} .0832319{col 50}{space 2} .0395431{col 61}{space 1}    2.10{col 70}{space 3}0.035{col 78}{space 4} .0057287{col 91}{space 3}  .160735
{txt}{space 23}l_sh_popedu_c {c |}{col 38}{res}{space 2} .0119498{col 50}{space 2} .0334499{col 61}{space 1}    0.36{col 70}{space 3}0.721{col 78}{space 4}-.0536108{col 91}{space 3} .0775103
{txt}{space 23}l_sh_popfborn {c |}{col 38}{res}{space 2} .0563678{col 50}{space 2} .0563135{col 61}{space 1}    1.00{col 70}{space 3}0.317{col 78}{space 4}-.0540048{col 91}{space 3} .1667403
{txt}{space 25}l_sh_empl_f {c |}{col 38}{res}{space 2}-.0313767{col 50}{space 2} .0587344{col 61}{space 1}   -0.53{col 70}{space 3}0.593{col 78}{space 4}-.1464941{col 91}{space 3} .0837407
{txt}{space 26}popdensity {c |}{col 38}{res}{space 2}-.0000188{col 50}{space 2} .0002905{col 61}{space 1}   -0.06{col 70}{space 3}0.948{col 78}{space 4}-.0005881{col 91}{space 3} .0005505
{txt}{space 23}nonwhiteshare {c |}{col 38}{res}{space 2} .4772059{col 50}{space 2}  2.91355{col 61}{space 1}    0.16{col 70}{space 3}0.870{col 78}{space 4}-5.233247{col 91}{space 3} 6.187659
{txt}{space 24}conservative {c |}{col 38}{res}{space 2}-1.077984{col 50}{space 2} .2799276{col 61}{space 1}   -3.85{col 70}{space 3}0.000{col 78}{space 4}-1.626632{col 91}{space 3}-.5293361
{txt}{space 28}moderate {c |}{col 38}{res}{space 2}-.1602677{col 50}{space 2} .2705768{col 61}{space 1}   -0.59{col 70}{space 3}0.554{col 78}{space 4}-.6905886{col 91}{space 3} .3700531
{txt}{space 25}collegegrad {c |}{col 38}{res}{space 2}-.1146419{col 50}{space 2} .2003978{col 61}{space 1}   -0.57{col 70}{space 3}0.567{col 78}{space 4}-.5074143{col 91}{space 3} .2781306
{txt}{space 31}_cons {c |}{col 38}{res}{space 2} 10.13559{col 50}{space 2} 5.432299{col 61}{space 1}    1.87{col 70}{space 3}0.062{col 78}{space 4}-.5115203{col 91}{space 3}  20.7827
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  
.   
.  quietly margins , dydx(l_sh_routine33a) at(logtotalrev90imp=(0(2)10)) vsquish
{txt}
{com}. 
.  marginsplot , graphregion(color(white)) 
{res}
{text}{p 2 6 2}Variables that uniquely identify margins: logtotalrev90imp{p_end}
{res}{txt}
{com}.  
.  
.  graph export "WisconsinPriority.png", replace
{txt}(file /Users/carlosxabel/Dropbox/HigherEd/Learning to love JOP data/Output/4Additionalempirical/WisconsinPriority.png written in PNG format)

{com}.  
.  
.  ************Survey on cities
.  cd ../..
{res}/Users/carlosxabel/Dropbox/HigherEd/Learning to love JOP data
{txt}
{com}.  
.  use "Data/msa_survey_indiv_merged.dta", clear
{txt}( )

{com}.  
.  
.  cd "Output/4Additionalempirical"
{res}/Users/carlosxabel/Dropbox/HigherEd/Learning to love JOP data/Output/4Additionalempirical
{txt}
{com}.  
.  
.  //Figure E.3 on Building ladders. Support for “building ladders” as a policy strategy to deal with technological changes (IV LP models with number of institutions in 1950s as instrument for HE invest- ments)
.  *1950s
.      ivregress 2sls buildladders ( c.l_sh_routine33a#c.logtotalrev90imp  l_sh_routine33a logtotalrev90imp  = countyinst100nottier1 R33a_1950_2000 R33a_1950_1990  c.R33a_1950_2000#c.countyinst100nottier1 c.R33a_1950_1990#c.countyinst100nottier1  )   l_shind_manuf_cbp l_sh_popedu_c  l_sh_popfborn l_sh_empl_f    popdensity i.state ///
>  female age3150 age5165 agegt65 above_hs_educ university_educ black latino income  [pweight=weight], r
{res}{txt}(sum of wgt is   7.6500e+03)
note: R33a_1950_1990 omitted because of collinearity
note: c.R33a_1950_1990#c.countyinst100nottier1 omitted because of collinearity
{res}
{txt}{col 1}Instrumental variables (2SLS) regression{col 51}Number of obs{col 67}= {res}     7,644
{txt}{col 1}{col 51}Wald chi2({res}28{txt}){col 67}= {res}    446.64
{txt}{col 1}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}{col 1}{col 51}R-squared{col 67}= {res}    0.0659
{txt}{col 51}Root MSE{col 67}=    {res} .47911

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}    Robust
{col 1}                        buildladders{col 38}{c |}      Coef.{col 50}   Std. Err.{col 62}      z{col 70}   P>|z|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
c.l_sh_routine33a#c.logtotalrev90imp {c |}{col 38}{res}{space 2} 2.743169{col 50}{space 2} 2.268543{col 61}{space 1}    1.21{col 70}{space 3}0.227{col 78}{space 4}-1.703094{col 91}{space 3} 7.189432
{txt}{space 36} {c |}
{space 21}l_sh_routine33a {c |}{col 38}{res}{space 2}-15.18029{col 50}{space 2} 12.92968{col 61}{space 1}   -1.17{col 70}{space 3}0.240{col 78}{space 4}-40.52201{col 91}{space 3} 10.16142
{txt}{space 20}logtotalrev90imp {c |}{col 38}{res}{space 2}-.7801814{col 50}{space 2} .6797041{col 61}{space 1}   -1.15{col 70}{space 3}0.251{col 78}{space 4}-2.112377{col 91}{space 3} .5520141
{txt}{space 19}l_shind_manuf_cbp {c |}{col 38}{res}{space 2}-.0077537{col 50}{space 2} .0116411{col 61}{space 1}   -0.67{col 70}{space 3}0.505{col 78}{space 4}-.0305698{col 91}{space 3} .0150624
{txt}{space 23}l_sh_popedu_c {c |}{col 38}{res}{space 2}-.0075283{col 50}{space 2} .0205024{col 61}{space 1}   -0.37{col 70}{space 3}0.713{col 78}{space 4}-.0477123{col 91}{space 3} .0326557
{txt}{space 23}l_sh_popfborn {c |}{col 38}{res}{space 2} .0117714{col 50}{space 2} .0324661{col 61}{space 1}    0.36{col 70}{space 3}0.717{col 78}{space 4} -.051861{col 91}{space 3} .0754038
{txt}{space 25}l_sh_empl_f {c |}{col 38}{res}{space 2} -.001995{col 50}{space 2} .0108715{col 61}{space 1}   -0.18{col 70}{space 3}0.854{col 78}{space 4}-.0233027{col 91}{space 3} .0193126
{txt}{space 26}popdensity {c |}{col 38}{res}{space 2}-.0000365{col 50}{space 2} .0000664{col 61}{space 1}   -0.55{col 70}{space 3}0.583{col 78}{space 4}-.0001667{col 91}{space 3} .0000938
{txt}{space 36} {c |}
{space 31}state {c |}
{space 27}Illinois  {c |}{col 38}{res}{space 2}  .050915{col 50}{space 2} .4130024{col 61}{space 1}    0.12{col 70}{space 3}0.902{col 78}{space 4}-.7585547{col 91}{space 3} .8603848
{txt}{space 28}Indiana  {c |}{col 38}{res}{space 2} .2688574{col 50}{space 2} .1717866{col 61}{space 1}    1.57{col 70}{space 3}0.118{col 78}{space 4}-.0678381{col 91}{space 3} .6055529
{txt}{space 24}Mississippi  {c |}{col 38}{res}{space 2}  .109334{col 50}{space 2} .1229946{col 61}{space 1}    0.89{col 70}{space 3}0.374{col 78}{space 4} -.131731{col 91}{space 3} .3503989
{txt}{space 27}Missouri  {c |}{col 38}{res}{space 2} .1642461{col 50}{space 2} .3275344{col 61}{space 1}    0.50{col 70}{space 3}0.616{col 78}{space 4}-.4777096{col 91}{space 3} .8062018
{txt}{space 27}New York  {c |}{col 38}{res}{space 2}-.0037358{col 50}{space 2} .5368322{col 61}{space 1}   -0.01{col 70}{space 3}0.994{col 78}{space 4}-1.055908{col 91}{space 3} 1.048436
{txt}{space 21}North Carolina  {c |}{col 38}{res}{space 2}-.0020657{col 50}{space 2} .4441073{col 61}{space 1}   -0.00{col 70}{space 3}0.996{col 78}{space 4}-.8724999{col 91}{space 3} .8683685
{txt}{space 31}Ohio  {c |}{col 38}{res}{space 2} .3878504{col 50}{space 2} .3068428{col 61}{space 1}    1.26{col 70}{space 3}0.206{col 78}{space 4}-.2135505{col 91}{space 3} .9892512
{txt}{space 21}South Carolina  {c |}{col 38}{res}{space 2}-.0442802{col 50}{space 2} .2516463{col 61}{space 1}   -0.18{col 70}{space 3}0.860{col 78}{space 4}-.5374979{col 91}{space 3} .4489375
{txt}{space 26}Tennessee  {c |}{col 38}{res}{space 2} .1439285{col 50}{space 2} .1243092{col 61}{space 1}    1.16{col 70}{space 3}0.247{col 78}{space 4}-.0997129{col 91}{space 3}   .38757
{txt}{space 30}Texas  {c |}{col 38}{res}{space 2}-.0348896{col 50}{space 2} .6262109{col 61}{space 1}   -0.06{col 70}{space 3}0.956{col 78}{space 4} -1.26224{col 91}{space 3} 1.192461
{txt}{space 25}Washington  {c |}{col 38}{res}{space 2}  .302333{col 50}{space 2} .5411117{col 61}{space 1}    0.56{col 70}{space 3}0.576{col 78}{space 4}-.7582264{col 91}{space 3} 1.362892
{txt}{space 36} {c |}
{space 30}female {c |}{col 38}{res}{space 2} .0325089{col 50}{space 2} .0141556{col 61}{space 1}    2.30{col 70}{space 3}0.022{col 78}{space 4} .0047645{col 91}{space 3} .0602534
{txt}{space 29}age3150 {c |}{col 38}{res}{space 2} .0668276{col 50}{space 2} .0235297{col 61}{space 1}    2.84{col 70}{space 3}0.005{col 78}{space 4} .0207102{col 91}{space 3}  .112945
{txt}{space 29}age5165 {c |}{col 38}{res}{space 2} .1948179{col 50}{space 2} .0203714{col 61}{space 1}    9.56{col 70}{space 3}0.000{col 78}{space 4} .1548907{col 91}{space 3} .2347452
{txt}{space 29}agegt65 {c |}{col 38}{res}{space 2} .2946149{col 50}{space 2} .0230839{col 61}{space 1}   12.76{col 70}{space 3}0.000{col 78}{space 4} .2493713{col 91}{space 3} .3398585
{txt}{space 23}above_hs_educ {c |}{col 38}{res}{space 2} .0698072{col 50}{space 2}  .020317{col 61}{space 1}    3.44{col 70}{space 3}0.001{col 78}{space 4} .0299866{col 91}{space 3} .1096277
{txt}{space 21}university_educ {c |}{col 38}{res}{space 2} .1484009{col 50}{space 2} .0237871{col 61}{space 1}    6.24{col 70}{space 3}0.000{col 78}{space 4}  .101779{col 91}{space 3} .1950228
{txt}{space 31}black {c |}{col 38}{res}{space 2}-.0290219{col 50}{space 2} .0222476{col 61}{space 1}   -1.30{col 70}{space 3}0.192{col 78}{space 4}-.0726265{col 91}{space 3} .0145826
{txt}{space 30}latino {c |}{col 38}{res}{space 2} .0487038{col 50}{space 2} .0394502{col 61}{space 1}    1.23{col 70}{space 3}0.217{col 78}{space 4}-.0286172{col 91}{space 3} .1260247
{txt}{space 30}income {c |}{col 38}{res}{space 2} .0099496{col 50}{space 2} .0025407{col 61}{space 1}    3.92{col 70}{space 3}0.000{col 78}{space 4} .0049699{col 91}{space 3} .0149292
{txt}{space 31}_cons {c |}{col 38}{res}{space 2} 5.057444{col 50}{space 2} 4.101685{col 61}{space 1}    1.23{col 70}{space 3}0.218{col 78}{space 4}-2.981712{col 91}{space 3}  13.0966
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 15 24}Instrumented:{space 2}c.l_sh_routine33a#c.logtotalrev90imp l_sh_routine33a logtotalrev90imp{p_end}
{p 0 15 24}Instruments:{space 3}l_shind_manuf_cbp l_sh_popedu_c l_sh_popfborn l_sh_empl_f popdensity 17.state 18.state 28.state 29.state 36.state 37.state 39.state 45.state 47.state 48.state 53.state female age3150 age5165 agegt65 above_hs_educ university_educ black latino income countyinst100nottier1 R33a_1950_2000 c.R33a_1950_2000#c.countyinst100nottier1{p_end}

{com}. eststo
{txt}({res}est5{txt} stored)

{com}.          quietly margins , dydx(l_sh_routine33a) at(logtotalrev90=(0(2)10)) vsquish
{txt}
{com}. 
.    marginsplot , graphregion(color(white)) 
{res}
{text}{p 2 6 2}Variables that uniquely identify margins: logtotalrev90imp{p_end}
{res}{txt}
{com}.    
.    graph export "CitiesBuildingladders.png", replace
{txt}(file /Users/carlosxabel/Dropbox/HigherEd/Learning to love JOP data/Output/4Additionalempirical/CitiesBuildingladders.png written in PNG format)

{com}.         
.  
.  
.         ///Figure E.4: ME of log routine exposure on trust in State Universities (linear model, 1-4)
>     reg  trust_StateUniv c.l_sh_routine33a##c.logtotalrev90imp  l_shind_manuf_cbp l_sh_popedu_c  l_sh_popfborn l_sh_empl_f    popdensity i.state, r 

{txt}Linear regression                               Number of obs     = {res}     7,748
                                                {txt}F(19, 7728)       =  {res}     2.66
                                                {txt}Prob > F          = {res}    0.0001
                                                {txt}R-squared         = {res}    0.0064
                                                {txt}Root MSE          =    {res}  .8115

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}    Robust
{col 1}                     trust_StateUniv{col 38}{c |}      Coef.{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}l_sh_routine33a {c |}{col 38}{res}{space 2}-3.985815{col 50}{space 2} 4.810173{col 61}{space 1}   -0.83{col 70}{space 3}0.407{col 78}{space 4}-13.41506{col 91}{space 3} 5.443427
{txt}{space 20}logtotalrev90imp {c |}{col 38}{res}{space 2}-.0741326{col 50}{space 2} .2311516{col 61}{space 1}   -0.32{col 70}{space 3}0.748{col 78}{space 4}-.5272524{col 91}{space 3} .3789872
{txt}{space 36} {c |}
c.l_sh_routine33a#c.logtotalrev90imp {c |}{col 38}{res}{space 2} .2972102{col 50}{space 2} .7402813{col 61}{space 1}    0.40{col 70}{space 3}0.688{col 78}{space 4}-1.153942{col 91}{space 3} 1.748362
{txt}{space 36} {c |}
{space 19}l_shind_manuf_cbp {c |}{col 38}{res}{space 2} .0149067{col 50}{space 2} .0052536{col 61}{space 1}    2.84{col 70}{space 3}0.005{col 78}{space 4} .0046082{col 91}{space 3} .0252053
{txt}{space 23}l_sh_popedu_c {c |}{col 38}{res}{space 2} .0259826{col 50}{space 2} .0094812{col 61}{space 1}    2.74{col 70}{space 3}0.006{col 78}{space 4} .0073969{col 91}{space 3} .0445683
{txt}{space 23}l_sh_popfborn {c |}{col 38}{res}{space 2} .0215118{col 50}{space 2} .0099276{col 61}{space 1}    2.17{col 70}{space 3}0.030{col 78}{space 4}  .002051{col 91}{space 3} .0409726
{txt}{space 25}l_sh_empl_f {c |}{col 38}{res}{space 2}-.0309391{col 50}{space 2} .0110862{col 61}{space 1}   -2.79{col 70}{space 3}0.005{col 78}{space 4} -.052671{col 91}{space 3}-.0092072
{txt}{space 26}popdensity {c |}{col 38}{res}{space 2} .0000328{col 50}{space 2} .0000149{col 61}{space 1}    2.20{col 70}{space 3}0.027{col 78}{space 4} 3.64e-06{col 91}{space 3} .0000619
{txt}{space 36} {c |}
{space 31}state {c |}
{space 27}Illinois  {c |}{col 38}{res}{space 2}-.0908521{col 50}{space 2} .1925186{col 61}{space 1}   -0.47{col 70}{space 3}0.637{col 78}{space 4}-.4682408{col 91}{space 3} .2865366
{txt}{space 28}Indiana  {c |}{col 38}{res}{space 2} .1213355{col 50}{space 2} .1861926{col 61}{space 1}    0.65{col 70}{space 3}0.515{col 78}{space 4}-.2436525{col 91}{space 3} .4863235
{txt}{space 24}Mississippi  {c |}{col 38}{res}{space 2}-.0200747{col 50}{space 2} .1500097{col 61}{space 1}   -0.13{col 70}{space 3}0.894{col 78}{space 4}-.3141344{col 91}{space 3}  .273985
{txt}{space 27}Missouri  {c |}{col 38}{res}{space 2}-.1243586{col 50}{space 2} .1901909{col 61}{space 1}   -0.65{col 70}{space 3}0.513{col 78}{space 4}-.4971843{col 91}{space 3}  .248467
{txt}{space 27}New York  {c |}{col 38}{res}{space 2}-.2531407{col 50}{space 2} .1985316{col 61}{space 1}   -1.28{col 70}{space 3}0.202{col 78}{space 4}-.6423163{col 91}{space 3}  .136035
{txt}{space 21}North Carolina  {c |}{col 38}{res}{space 2}-.1749123{col 50}{space 2} .1944492{col 61}{space 1}   -0.90{col 70}{space 3}0.368{col 78}{space 4}-.5560854{col 91}{space 3} .2062608
{txt}{space 31}Ohio  {c |}{col 38}{res}{space 2} -.040189{col 50}{space 2} .1951852{col 61}{space 1}   -0.21{col 70}{space 3}0.837{col 78}{space 4}-.4228049{col 91}{space 3} .3424268
{txt}{space 21}South Carolina  {c |}{col 38}{res}{space 2}-.1511955{col 50}{space 2} .1875064{col 61}{space 1}   -0.81{col 70}{space 3}0.420{col 78}{space 4}-.5187588{col 91}{space 3} .2163678
{txt}{space 26}Tennessee  {c |}{col 38}{res}{space 2}-.0263165{col 50}{space 2} .1432348{col 61}{space 1}   -0.18{col 70}{space 3}0.854{col 78}{space 4}-.3070956{col 91}{space 3} .2544625
{txt}{space 30}Texas  {c |}{col 38}{res}{space 2}-.6415363{col 50}{space 2} .2581959{col 61}{space 1}   -2.48{col 70}{space 3}0.013{col 78}{space 4} -1.14767{col 91}{space 3}-.1354024
{txt}{space 25}Washington  {c |}{col 38}{res}{space 2}-.4521979{col 50}{space 2} .2754086{col 61}{space 1}   -1.64{col 70}{space 3}0.101{col 78}{space 4}-.9920733{col 91}{space 3} .0876776
{txt}{space 36} {c |}
{space 31}_cons {c |}{col 38}{res}{space 2} 4.355686{col 50}{space 2} 1.564122{col 61}{space 1}    2.78{col 70}{space 3}0.005{col 78}{space 4} 1.289583{col 91}{space 3} 7.421788
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.  quietly margins , dydx(l_sh_routine33a) at(logtotalrev90=(0(2)10)) vsquish
{txt}
{com}. marginsplot , graphregion(color(white)) 
{res}
{text}{p 2 6 2}Variables that uniquely identify margins: logtotalrev90imp{p_end}
{res}{txt}
{com}.    
. 
. graph export "CitiesTruststateUnis.png", replace
{txt}(file /Users/carlosxabel/Dropbox/HigherEd/Learning to love JOP data/Output/4Additionalempirical/CitiesTruststateUnis.png written in PNG format)

{com}. 
. 
. //Figure E.5: ME of log routine exposure on trust in State Governments (linear model, 1-4)
.          reg trust_stategov c.l_sh_routine33a##c.logtotalrev90imp  l_shind_manuf_cbp l_sh_popedu_c  l_sh_popfborn l_sh_empl_f  , r 

{txt}Linear regression                               Number of obs     = {res}     7,748
                                                {txt}F(7, 7740)        =  {res}    15.89
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0145
                                                {txt}Root MSE          =    {res} .86254

{txt}{hline 37}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 38}{c |}{col 50}    Robust
{col 1}                      trust_stategov{col 38}{c |}      Coef.{col 50}   Std. Err.{col 62}      t{col 70}   P>|t|{col 78}     [95% Con{col 91}f. Interval]
{hline 37}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}l_sh_routine33a {c |}{col 38}{res}{space 2} 1.228595{col 50}{space 2} 2.924179{col 61}{space 1}    0.42{col 70}{space 3}0.674{col 78}{space 4}-4.503588{col 91}{space 3} 6.960778
{txt}{space 20}logtotalrev90imp {c |}{col 38}{res}{space 2}-.0373835{col 50}{space 2} .1542318{col 61}{space 1}   -0.24{col 70}{space 3}0.808{col 78}{space 4}-.3397195{col 91}{space 3} .2649525
{txt}{space 36} {c |}
c.l_sh_routine33a#c.logtotalrev90imp {c |}{col 38}{res}{space 2} -.026421{col 50}{space 2} .4824528{col 61}{space 1}   -0.05{col 70}{space 3}0.956{col 78}{space 4} -.972159{col 91}{space 3}  .919317
{txt}{space 36} {c |}
{space 19}l_shind_manuf_cbp {c |}{col 38}{res}{space 2} .0014953{col 50}{space 2} .0022337{col 61}{space 1}    0.67{col 70}{space 3}0.503{col 78}{space 4}-.0028834{col 91}{space 3}  .005874
{txt}{space 23}l_sh_popedu_c {c |}{col 38}{res}{space 2}-.0038399{col 50}{space 2} .0036957{col 61}{space 1}   -1.04{col 70}{space 3}0.299{col 78}{space 4}-.0110845{col 91}{space 3} .0034046
{txt}{space 23}l_sh_popfborn {c |}{col 38}{res}{space 2} .0054131{col 50}{space 2} .0023218{col 61}{space 1}    2.33{col 70}{space 3}0.020{col 78}{space 4} .0008617{col 91}{space 3} .0099644
{txt}{space 25}l_sh_empl_f {c |}{col 38}{res}{space 2}-.0046747{col 50}{space 2} .0066975{col 61}{space 1}   -0.70{col 70}{space 3}0.485{col 78}{space 4}-.0178037{col 91}{space 3} .0084543
{txt}{space 31}_cons {c |}{col 38}{res}{space 2} 2.616677{col 50}{space 2} .9304637{col 61}{space 1}    2.81{col 70}{space 3}0.005{col 78}{space 4} .7927162{col 91}{space 3} 4.440637
{txt}{hline 37}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.  quietly margins , dydx(l_sh_routine33a) at(logtotalrev90=(0(2)10)) vsquish
{txt}
{com}. marginsplot , graphregion(color(white))
{res}
{text}{p 2 6 2}Variables that uniquely identify margins: logtotalrev90imp{p_end}
{res}{txt}
{com}. 
.  
. graph export "CitiesTruststategov.png", replace
{txt}(file /Users/carlosxabel/Dropbox/HigherEd/Learning to love JOP data/Output/4Additionalempirical/CitiesTruststategov.png written in PNG format)

{com}. 
. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}/Users/carlosxabel/Dropbox/HigherEd/Learning to love JOP data/Output/4Additionalempirical/4Log.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}27 Feb 2024, 16:27:38
{txt}{.-}
{smcl}
{txt}{sf}{ul off}